Face location detection

ABSTRACT

The location of a face is detected from data about a scene. A 3D surface model from is obtained from measurements of the scene. A 2D angle data image is generated from the 3D surface model. The angle data image is generated for a virtual lighting direction, the image representing angles between a ray directions from a virtual light source direction and normal to the 3D surface. A 2D face location algorithm is applied to each of the respective 2D images. In an embodiment respective 2D angle data images for a plurality of virtual lighting directions are generated and face locations detected from the respective 2D images are fused.

CROSS-REFERENCE TO PRIOR APPLICATIONS

This application is the U.S. National Phase application under 35 U.S.C.§371 of International Application Serial No. PCT/IB2012/052038, filed onApr. 23, 2012, which claims the benefit of European Application SerialNo. 11164162.7, filed on Apr. 28, 2011. These applications are herebyincorporated by reference herein.

FIELD OF THE INVENTION

The invention relates to a system and method for determining thelocation of a face from measurements that produce a 3 dimensionalsurface model of a scene containing the face.

BACKGROUND OF THE INVENTION

U.S. Pat. No. 7,436,988 describes a face authentication and recognitionmethod that uses 2 dimensional images to form a 3 dimensional model, andverification of the identity of a person by comparison of the 3dimensional model with reference data. A face profile line is extractedfrom the intersection of the symmetry plane of the face with the 3Dmodel and properties of this line are used for verification of theidentity of a person. The symmetry plane is found by forming a mirroredversion of the 3D model and positioning the original 3D model and themirrored version relative to each other in a way results in a bestmatch. The procedure for finding the symmetry plane implicitlydetermines information relating to the location of the face, on theassumption that the face is the only substantially symmetric object inthe modeled space. However, U.S. Pat. No. 7,436,988 does not discuss theproblem of location determination.

Face recognition and face location determination are different tasksthat involve different considerations. Face recognition is inherentlyperson specific: the essence of face recognition is that differentpersons should be distinguished. Face location determination, on theother hand, is preferably person independent: the location should bedetermined independent of the unique personal features of the face.

SUMMARY OF THE INVENTION

Among others, it is an object to provide for system and method fordetermining the location of a face from a 3 dimensional model of a scenecontaining the face.

A method according to claim 1 is provided. Herein a 3D surface model ofa scene containing a face is used as a starting point for facelocalization, i.e. detection of the location of a face in a scene. The3D surface model may be derived from 2D optical imaging for example.From the 3D surface model a 2D angle data image is generated, whichrepresents angle data, of angles between normals to the modeled 3Dsurface and incidence directions according to a direction of a virtuallight source. The angle data image is used in the execution of a 2D facelocation algorithm.

In this way, an available and tested 2D face location algorithm can beused for determination of the face location in a 3D model. Reliablemeasurements of optical surface properties of the face is not needed.Even if the 3D surface model is derived from 2D optical images, use ofthe 3D surface model makes face location detection more robust againstthe effects of reflection properties and color of the surface.Preferably, the pixel values of the generated 2D image are determinedonly from geometric information about the modeled 3D surface at 3Dpoints that are in view at respective pixel locations of the generated2D image, without using non-geometric optical properties.

In an embodiment a plurality of respective 2D images with angle data forrespective different virtual lighting directions is generated and theface location detection algorithm is applied to each of the respective2D images. The resulting location detections may be combined to providea detected face location. The resulting location detections may be fusedfor example by taking the average of detected positions for differentdirections, optionally after removing outliers or by selecting a medianor other representative one of the detected positions for differentdirections. Combination may also include a clustering step, involvingassignment of location detections to selected clusters and fusinglocation detections within a cluster.

In an embodiment a plurality of 2D images is generated for a sameviewing direction for said respective directions. In this way, themethod is made more robust. Moreover it is made possible to determineaverages of the face location in the 2D images. Alternatively, arbitrarycombinations of viewing direction for said respective directions may beused. In an embodiment images for a plurality of different viewingdirection are generated. This makes the method robust against variationof the rotation of the face.

In an embodiment the 2D face localization algorithm may comprisedetermining a plurality of sums of the angle data over respectiveregions in the image, comparing the sums with thresholds and combiningthe results of said comparisons.

A successful algorithm for face location determination from 2dimensional images is known from an article by Viola et al, titled“Robust Real-Time Face Detection”, published in the InternationalJournal of Computer Vision 57(2), 2004 pages137-154. Viola et al use acombination of sums of pixel values in various rectangular image areasto decide the location of a face. The combination of rectangular imageareas is set by means of computer learning. In a learning phase, thesystem is presented with a large collection of examples andcounterexamples of faces and a large collection of rectangles ofdifferent size and position that may be used. The AdaBoost computerlearning technique is used to select a combination of rectangles thatprovides robust results. The algorithm described by Viola et al may beused for example, using training results for 2D images, without havingto go through a new training procedure.

The 3D surface model may be obtained from a storage device where it hasbeen stored after it has been generated. In an embodiment informationfrom one or more 2D images of light received from the scene is used toobtain the 3D surface model. Structured light may be used to light thescene. By first converting this information to a 3D surface model andthen converting back to 2D images wherein surface orientation ratherthan direct optical image properties are used, the method makes itunnecessary to obtain reliable information about the optical surfaceproperties of the face. Alternatively, other measuring techniques, suchas 3D tomographic techniques may be used to obtain the 3D surface, forexample indirectly from a 3D volumetric model.

BRIEF DESCRIPTION OF THE DRAWING

These and other objects and advantageous aspects will become apparentfrom a description of exemplary embodiments, using the followingfigures.

FIG. 1 shows a face data processing system

FIG. 2 shows a flow chart of face location determination

FIG. 3 shows a processing architecture

DETAILED DESCRIPTION OF EXEMPLARY EMBODIMENTS

FIG. 1 shows a face data processing system, comprising a structuredlight source 10, a camera 12 and a processor 14. Structured light source10 and camera 12 is coupled to processor 14. Although structured lightsource 10 and camera 12 are shown by way of example, it should beappreciated that other devices for capturing 3D geometric informationmay be used. The structured light source 10 may be configured to projectan array of dots or stripes onto an object, i.e. to transmit light onlyin an array of mutually separate ray directions, or an array of mutuallyseparate ray planes. Structured light source 10 may be configured torealize the projection by means of dynamical scanning along a pattern ofdirections or using projection through a static mask for example.

An operational configuration is shown, wherein structured light source10 and camera 12 are directed at a scene that contains a face 16. Inoperation structured light source 10 illuminates a scene with astructured light pattern or patterns and camera 12 captures an image ofthe scene while it is illuminated by structured light source 10.Structured light source 10 supplies information representing one or moreresulting images to processor 14.

Processor 14 processes the image or images to form a 3D surface model ofthe scene. Methods for doing so are known per se. Processor 14 may be aprogrammable processor, comprising a computer program to make processor14 do this. Processor 14 may in fact be a processing system, comprisinga plurality of computers that perform different parts of the tasks thatprocessor 14 is configured to perform. As used herein, processor 14 willbe said to be configured to perform operations when it has a programthat will make it perform these operations. However, processor 14 willalso be said to be configured to perform operations if it containsdedicated circuits designed to perform the operations.

Processor 14 may be configured to detect pixel locations where surfacepoints in the scene are visible that are illuminated by light fromstructured light source 10. Furthermore, processor 14 may be configuredto identify for each such pixel the position of the illuminating lightwithin the pattern of structured light from structured light source 10.Given the camera pose and the geometry of structured light source 10,the position in the pattern and the pixel position each define a raydirection in 3D space, the directions intersecting at the illuminatedobject point. Processor 14 computes the 3D coordinates of the objectpoints from the pixel positions and the positions in the pattern.

Processor 14 may approximate the surface of the object by interpolationbetween the computed positions, e.g. by approximating the surfacebetween neighboring detected points as planar triangles. From such aninterpolation interpolated 3D coordinates of any surface points can bedetermined, as well as the surface normal at those surface points. Suchdeterminations can be applied to pixels of the image captured by camera12, but also to virtual images obtainable from other camera poses.Processor 14 is configured to use this information to determine thelocation of a face in 3D space.

FIG. 2 shows a flow chart of a process for determining the facelocation. The flow chart will be described in terms of actions performedby processor 14. It should be understood that processor 14 is“configured” to perform these actions. In a first step 21, processor 14selects a first direction relative to the 3D model. The selected firstdirection will be called a virtual camera direction. In an embodimentthe first direction may correspond to the direction of camera 12.

In a second step 22, processor 14 defines an image comprising an arrayof pixels. The array of pixels is associated with an array of ray pathshaving directions defined relative to the selected first direction.Processor 14 uses the 3D surface model obtained from structured lightingto determines for each pixel the 3D point of intersection of the raypath for that pixel with the 3D surface and the normal of the surface atthat 3D point.

In a third step 23, processor 14 selects a second direction relative tothe 3D model. The selected second direction will be called a virtuallight source direction. (It should be emphasized that the words virtuallight source direction are used only as a label for this direction,without implying that a virtual light source must be used to generateimages, or that the generated images represent images that could beobtained with a specific light source).

In a fourth step 24 processor 14 determines angles between the seconddirection and the normals for the respective pixel, or at leastprocessor 14 determines quantities that are a function of the angle,such as cosines of the angles. The angle or quantity that is a functionof the angle will be called angle data. The angle data associated withrespective positions in the image forms an angle data image. In anembodiment, angles with the derived directions that are all the same asthe second direction may be used for each of the image positions,simulating the angles with rays from a virtual illumination source atinfinity. Alternatively, angles with directions that are derivedindirectly from the second direction may be determined. The deriveddirections may be determined for example by assuming a source point on aline from the scene along the second direction, determining lines fromthat source point to respective 3D points that are visible at respectivepositions in a 2D image plane and using the directions of these lines asthe derived directions. Typically, the angle data is indicative of theangle, so that the angles could be determined from the angle datawithout requiring knowledge of optical surface properties.

In a fifth step 25, processor 14 applies a 2D face location detectionalgorithm to the angle data. A conventional algorithm, such as thealgorithm described by Viola at al may be used, using combinations ofdetector rectangles that have been selected by training on normal 2Dcamera images (showing light intensity). A standard combination ofdetector rectangles may be used that is available from training thedetection algorithms with 2D camera images. Briefly, the 2D facelocation detection algorithm may comprise determining respective sums ofthe angle data for each detector rectangle in a predetermined set ofdetector rectangles in the image. The respective sums may be computed byincrementally determining successive reference sums for respective pixellocations, the reference sum for each pixel location being a sum ofpixel values in a rectangle with that pixel location and a corner of theimage as diagonally opposed corners. The determination of the referencesums may be followed by selecting the reference sum values V(LL), V(LR),V(UL), V(UR) for the corners (LL=Lower Left etc to UR=Upper Right) of adetector rectangle and subtracting sums of the reference sum values forpairs of diagonally opposing corners of the detector rectangles.Although an example has been given for rectangles, it should beappreciated that a similar technique can be applied for regions of othershapes, such as parallelograms, by using reference sums for other shapesfrom pixel locations to the corner of the image with different shapes.Processor 14 may then compare the sums for the detector triangles withrespective predetermined thresholds (which may have been obtained bylearning) and combine the results of the comparisons to form a detectionresult. The detection result indicates at least a 2D central pixellocation of a detected face in the image, if any.

Third to fifth steps 23-25 may be repeated for different selections ofthe second directions. In a sixth step 26, processor 14 determineswhether a predetermined number of second directions have been selected.If not, processor 14 repeats from third step 23 for a new seconddirection. When the steps have been performed for the predeterminednumber of second directions, processor 14 proceeds to seventh step 27.

In a seventh step 27, processor 14 combines the results obtained fordifferent second directions. The combination may be realized by fusionof the results. In an embodiment fusion may involve determining a medianvalue of the 2D central pixel locations determined for different seconddirections and using the median as the fused detection result.Alternatively, an average may be computed, optionally after removingoutliers. These and other techniques for fusing a plurality of resultsfor a same measured parameter are known per se. In this case combinationof the detection results produces a nominal pixel position, which is amedian or average pixel position, or similar result of combining aplurality of 2D locations and rectangle sizes. Subsequently, processor14 determines the 3D position associated with the nominal pixellocation. In another embodiment, respective 3D positions may bedetermined for 2D images with different individual second directions andthe respective 3D positions may be used to determine a nominal 3Dposition. The combining of seventh step 27 may also include clustering.Clustering techniques, such as the k-means algorithm are known per se,and may involve determining a plurality of nominal face locations forrespective clusters, assigning each detected face location to thecluster with the closest nominal face location and fusing the detectedface location that have been assigned to a same cluster to produce thecombined result.

In the methods described by means of the flow-chart of FIG. 2 a 3D facelocation is preferably determined without using conventional imageinformation (dependent on light intensity and object reflectivity) inthe 2D location determination. The 3D surface model may be a purelygeometric model, which defines only spatial locations and directionvectors, The direction vectors in the 3D model may be the result ofmeasurements, or they may be defined by interpolation. If the 3D modelcontains optical reflectivity information (e.g. reflection coefficients,possibly as a function of wavelength of diffuse or even specular lightreflection), this information may be ignored in the generation of the 2Dimages for 2D face location detection.

Of course, light intensity and object reflectivity may play a role inthe formation of the camera image during illumination with structuredlight, but this information is lost in the 3D surface model that is usedin second to fourth steps 22-24, where only geometric information isused. Avoidance of the use of such conventional image information makesface location detection more independent of differences betweenindividual faces, such as face color. In contrast to face recognition,suppression of individuality is advantageous for face locationdetection. At the same time, use of images of angle data makes itpossible to use conventional 2D face location detection algorithmswithout extensive learning and use of a plurality of second directionsmakes the method robust against effects of face rotation.

Processor 14 is configured to perform the described steps for exampleunder control of a computer program, or by using electronic circuitsdesigned to perform the steps, or by using a mix of a computer programand circuits, performing respective parts of the steps.

Although an embodiment has been described wherein a plurality of 2Dimages for a plurality of first and/or second directions are generatedand used for face location detection, it should be appreciated that facelocation detection from a single 2D images for a specific first andsecond direction may suffice. In this case the detected 2D face locationcan be converted to a 3D location by means of information about the 3Dsurface model point that was images to the detected 2D face location.Use of a plurality of virtual lighting directions makes the methodrobust against dependence on virtual lighting direction.

FIG. 3 shows an embodiment of the architecture of processor 14comprising a 3D surface model generator 30, a 2D image generator 32, a2D face location detector 34, a data combiner 36, a 2D-3D convertor 38and a control module 39. 3D surface model generator 30 has an input forreceiving 2D image data from the camera (not shown) and an output forsupplying 3D surface model data. 2D image generator 32 has an inputcoupled to the output of 3D surface model generator 30 and outputs foroutputting a 2D angle data image and a 2D image of 3D coordinates (ordepth data). 2D face location detector 34 has an input coupled to theoutput for the 2D angle data image. An embodiment is shown wherein 2Dface location detector 34 comprises a summer 340, a comparator 342 and acombination module 344 coupled in series.

2D face location detector 34 has an output for outputting informationrepresenting a detection result, including information indicating adetected face location and/or face region in its input image. Datacombiner 36 has an input for receiving the information indicating thedetected face location and/or face region and an output for outputtinginformation indicating a nominal location and/or region. Data combiner36 may be a data fuser. As used herein, fusing is any operation thatcombines different data about the same parameter to determine a value ofthe parameter, including for example averaging detected face locationand/or locations of face regions (as used here, average is used broadly,including a mean value, a median value, or an mean value obtained aftereliminating outliers). A data combiner is a module, for example aprogram module that combines data in this way. In the embodiment whereina single first and second direction is used, data combiner 36 may beomitted.

2D-3D convertor has inputs coupled to the output for outputtinginformation indicating an average detected face location and/or faceregion and to the output of 2D image generator 32 for outputting the 2Dimage of 3D coordinates (or depth data). Control module 39 is coupled to3D surface model generator 30, 2D image generator 32, 2D face locationdetector 34, averaging module 36, and 2D-3D convertor. Control module 39is configured to make 2D image generator 32 generate images for aplurality of angles from the same 3D surface model and to make averagingmodule 36 determine an average (i.e. a mean value or a median value)from 2D face detection results for the plurality of angles. Asexplained, the various elements of the architecture may be softwaremodules executed by a programmable processor, or electronic circuits ora combination thereof. The elements perform the steps of the flow-chartof FIG. 2: control module 39 may perform first, third and sixth steps21, 23, 26, 2D image generator 32 may perform second and fourth steps22, 24, 2D face location detector 34 may perform fifth step 25 and datacombiner 36 may perform seventh step 27. Not all steps need be performedin the same device. For example 3D surface model generator 30 may beperformed in one device on-line during 2D image capture and thensupplied from that device to another device that contains the otherelements of FIG. 3, for off-line processing.

In an embodiment first to seventh step 21-27 may be repeated for aplurality of different selected first directions. In this way aplurality of 3D positions associated with nominal pixel locations fordifferent second direction is determined. From these 3D positions anominal 3D position may be determined (e.g. a median or average of the3D positions for different first directions). In an embodiment, the samefirst direction may be used for each of the image positions, simulatinga virtual camera at infinity. Alternatively, derivative first directionsmay be used that are derived from the first direction. The deriveddirections may be determined for example by assuming a view point on aline from the scene along the first direction, determining lines fromthat view point through 2D positions in an imaging plane and usingintersections of those lines with the modeled 3D surface as surfacepoints.

When 3D positions are determined from nominal detected face locationsfor a plurality of 2D images obtained with the same second direction,the nominal detected face location may be determined in 2D beforedetermining the 3D surface location associated with this nominal 2Dsurface location. When 3D positions are determined from the facelocation detections for individual 2D images, and the nominal locationis determined subsequently in 3D it is not necessary to have sets of 2Dimages obtained with the same second direction: first and third step 21,23 may be combined to select pairs of first and second directions in anyway.

In an embodiment, a 2D face location detection algorithm that outputsdetected face location in the form of indications of 2D image regionswherein faces have been detected may be used in fifth step 25. Anindication of a 2D image region may indicate a rectangle with edges atthe upper, lower, left and right sides of the detected face for example.In this embodiment processor 14 may be configured to us a centre pointof the rectangle as the detected face location. These may each be usedas a single detected face location.

Furthermore, processor 14 may be configured to use the indication of a2D image region to determine a 3D surface region wherein a face has beendetected, for example from the 3D coordinates associated with pixellocations within the indicated 2D image region, or from the 3Dcoordinates associated with pixel locations along a boundary of theindicated 2D image region. In an embodiment, processor 14 is configuredto determine a nominal 2D image region for a detected face from theindicated 2D image regions obtained with respective second directions,for example by determine median or average values of the positions ofedges of rectangular regions, or taking medians or averages of thedistance to the boundary from the nominal centre position.

The described steps may be followed by further steps to determine theorientation, boundary and/or size of the face. Processor 14 may beconfigured to use the 3D face location in a further step to determine asearch area for performing a search for the location of features of theface, such as a nose, a mouth and eyes. The locations of such featuresmay be used to determine face orientation and/or size.

While the exemplary embodiments have been illustrated and described indetail in the drawings and foregoing description, such illustration anddescription are to be considered illustrative or exemplary and notrestrictive; the invention is not limited to the disclosed embodiments.

Although an embodiment has been shown wherein structured light is usedto obtain the 3D surface model, it should be appreciated that othertechniques may be used to obtain such a model. For example astereoscopic technique may be used, wherein images from a plurality ofcameras at different locations are used, or an SLAM technique may beused, wherein a plurality of images from the same moving camera is usedto derive a 3D surface model. A depth imaging camera (range camera) maybe used that is configured to form an image based on ‘time of flight’measurement if a plurality of directions corresponding to respectivepixels. Non-optical techniques, such as nuclear magnetic imaging,ultrasound echography, X-ray tomography may be used as alternatives.When a volumetric technique is used, the resulting volumetric model maybe used to derive the 3D surface model. In an embodiment wherein the 3Dsurface model is derived from 2D camera images, the first direction isselected equal to the viewing direction of one or more of the 2D cameraimages. In this case the angles with the normals defined by the 3Dsurface model are used to generate images for face location detectioninstead of, or in addition to the original 2D camera image of observedlight intensity from the same viewing direction.

The 3D surface model may be stored in a storage device (e.g. a magneticdisk or a semi-conductor memory) from where it may be read in order toobtain it for the described face location detection. Alternatively the3D surface model may be obtained directly from measurements when facelocation detection is performed or it may be obtained by deriving the 3Dsurface model from 2D images or a volumetric model when face locationdetection is performed.

Dependent on the desired application the resulting detected 3D facelocation may be used in different ways. It may be used to perform a facerecognition algorithm to verify or find the identity of a person fromthe 3D surface model for example. As another example it may serve asinput for further processing of the 3D surface model, such as thedetermination of the shape of a mask to be fitted over the face andselection of a suitable mask from a predetermined set of mask or controlof manufacture of such a mask. As another example it may be used tocontrol processing of volumetric data such as an NMI image, for exampleto control the location of 3D regions in the volumetric model for whichmeasurements are obtained.

Other variations to the disclosed embodiments can be understood andeffected by those skilled in the art in practicing the claimedinvention, from a study of the drawings, the disclosure, and theappended claims. In the claims, the word “comprising” does not excludeother elements or steps, and the indefinite article “a” or “an” does notexclude a plurality. A single processor or other unit may fulfill thefunctions of several items recited in the claims. The mere fact thatcertain measures are recited in mutually different dependent claims doesnot indicate that a combination of these measured cannot be used toadvantage. A computer program may be stored/distributed on a suitablemedium, such as an optical storage medium or a solid-state mediumsupplied together with or as part of other hardware, but may also bedistributed in other forms, such as via the Internet or other wired orwireless telecommunication systems. Any reference signs in the claimsshould not be construed as limiting the scope.

The invention claimed is:
 1. An image processing method wherein alocation of a face is detected, the method comprising obtaining a 3Dsurface model from measurements of a scene; generating a 2D image ofangle data from the 3D surface model, the 2D image representing angledata, the angle data for each respective image point in the 2D imagebeing selected dependent on an angle between an incidence directionderived from a virtual lighting direction and a normal to the 3D surfaceat a point on the 3D surface that is in view in the 2D image at theimage point; applying a 2D face location algorithm to the 2D imagegenerating a plurality of respective 2D images from the 3D surfacemodel, each representing the angle data for a respective virtuallighting direction; applying the 2D face location algorithm to each ofthe respective 2D images; combining face locations detected from therespective 2D images.
 2. A method according to claim 1, comprisinggenerating a plurality of said respective 2D images for a same viewingdirection for said respective virtual lighting directions.
 3. A methodaccording to claim 2, comprising generating respective pluralities ofsaid respective 2D images from, each plurality from a different viewingdirection, each plurality comprising respective 2D images for aplurality of mutually different respective virtual lighting directions.4. A method according to claim 2, comprising determining an averagedetected 2D location for said plurality of respective 2D images anddetermining a 3D surface location associated with said average detected2D location according to the 3D surface model.
 5. A method according toclaim 1, comprising determining respective 3D surface locationsassociated with respective detected 2D face locations according to the3D surface model and determining an average of the respective 3D surfacelocations.
 6. A method according to claim 1, wherein the 3D surfacemodel is obtained using information from one or more further 2D imagesof light received from the scene.
 7. A method according to claim 6,comprising lighting the scene by means of structured light.
 8. A methodaccording to claim 6, wherein the respective 2D images are generated fora viewing direction corresponding to a viewing direction of the one ormore further 2D images of light received from the scene.
 9. A methodaccording to claim 6, wherein the respective 2D images are generatedindependent of light reflection properties associated with the points onthe 3D surface, if any.
 10. A method according to claim 1, comprisingusing a face location detected by the 2D face location algorithm and the3D surface model to determine a 3D location of a point on the 3D surfacethat is visible at the detected face location.
 11. A non-transitorycomputer program product, comprising a program of instruction for aprogrammable processor that, when executed by the programmableprocessor, will cause the programmable processor to perform the methodof claim
 1. 12. An image processing method wherein a location of a faceis detected, the method comprising obtaining a 3D surface model frommeasurements of a scene; generating a 2D image of angle data from the 3Dsurface model, the 2D image representing angle data, the angle data foreach respective image point in the 2D image being selected dependent onan angle between an incidence direction derived from a virtual lightingdirection and a normal to the 3D surface at a point on the 3D surfacethat is in view in the 2D image at the image point; applying a 2D facelocation algorithm to the 2D image, wherein applying said 2D facelocation algorithm comprises determining a plurality of sums of theangles over respective regions in the image, comparing the sums withthresholds and combining the results of said comparisons.
 13. An imageprocessing system, the system comprising a 2D image generator configuredto generate a number of 2D images from a 3D surface model obtained for ascene, the 2D images representing angle data, the angle data for eachrespective image point in the 2D image being selected dependent on anangle between an incidence direction derived from a virtual lightingdirection and a normal to the 3D surface at a point on the 3D surfacethat is in view in the image at the image point; a 2D face locationdetector configured to apply a 2D face location detection algorithm toeach of the respective 2D images, wherein the 2D image generator isconfigured to generate a plurality of respective 2D images from the 3Dsurface model, each respective 2D image representing the angle data fora respective virtual lighting direction, the image processing systemcomprising a data combiner configured to combine face location detectionresults detected by 2D face location detector from the plurality ofrespective 2D images.
 14. An image processing system according to claim13, comprising a camera and a 3D model generator configured to generatethe 3D surface model using image data from said camera.
 15. An imageprocessing system, the system comprising a 2D image generator (32)configured to generate a number of 2D images from a 3D surface modelobtained for a scene, the 2D images representing angle data, the angledata for each respective image point in the 2D image being selecteddependent on an angle between an incidence direction derived from avirtual lighting direction and a normal to the 3D surface at a point onthe 3D surface that is in view in the image at the image point; a 2Dface location detector (34) configured to apply a 2D face locationdetection algorithm to each of the respective 2D images by determining aplurality of sums of the angles over respective regions in the image,comparing the sums with thresholds and combining the results of saidcomparisons.